title: IBD-Groupon: an efficient method for detecting group-wise identity-by-descent regions simultaneously in multiple individuals based on pairwise IBD relationships.
abstract: BACKGROUND
  Detecting IBD tracts is an important problem in genetics. Most of the existing methods focus on detecting pairwise IBD tracts, which have relatively low power to detect short IBD tracts. Methods to detect IBD tracts among multiple individuals simultaneously, or group-wise IBD tracts, have better performance for short IBD tracts detection. Group-wise IBD tracts can be applied to a wide range of applications, such as disease mapping, pedigree reconstruction and so forth. The existing group-wise IBD tract detection method is computationally inefficient and is only able to handle small datasets, such as 20, 30 individuals with hundreds of SNPs. It also requires a previous specification of the number of IBD groups, or partitions of the individuals where all the individuals in the same partition are IBD with each other, which may not be realistic in many cases. The method can only handle a small number of IBD groups, such as two or three, because of scalability issues. What is more, it does not take LD (linkage disequilibrium) into consideration.
  
  
  RESULTS
  In this work, we developed an efficient method IBD-Groupon, which detects group-wise IBD tracts based on pairwise IBD relationships, and it is able to address all the drawbacks aforementioned. To our knowledge, our method is the first practical group-wise IBD tracts detection method that is scalable to very large datasets, for example, hundreds of individuals with thousands of SNPs, and in the meanwhile, it is powerful to detect short IBD tracts. Our method does not need to specify the number of IBD groups, which will be detected automatically. And our method takes LD into consideration, as it is based on pairwise IBD tracts where LD can be easily incorporated.
authors.affiliation: Computational Genomics, IBM TJ Watson Research, Yorktown Heights, NY 10598, USA. dhe@us.ibm.com
authors.forename: Dan
authors.initials: D
authors.lastname: He
mesh_terms: D000465:Algorithms; D000483:Alleles; D005828:Genetics, Population / Q000379:methods; D006801:Humans; D008390:Markov Chains; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide